Skip to main content
Glama
kitepon-rgb

aiterm-mcp

composer_agent

Launch a persistent TUI for Grok Build's Composer model. Submit prompts asynchronously and retrieve results when ready.

Instructions

【Grok Build の Composer モデル (既定 grok-composer-2.5-fast)】の対話エージェント TUI を永続端末に起動する。turn は pty_send で送る(自動で非ブロック dispatch になる)。起動して投げたら投げっぱなしでよい=親はここで待たない。完了通知は起動応答の wait_command(初回prompt時)または pty_send dispatch 後の aiterm-wait --session --cursor を親のターンを塞がない別プロセスとして起動して受ける(exit 0=done / 3=timeout(既定600秒・未完了) / 4=closed。receiptのoutcomeが正で、done以外は未完了。ポーリング不要・foreground実行はしない)。結果回収は pty_read(agent_transcript:true)。model を引数で指定可。reasoning_effort は非対応(指定はエラー)。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cwdNo作業ディレクトリ(対象リポのルート等・任意)
modelNo起動モデル。省略時は grok-composer-2.5-fast
promptNo起動時に渡す初手プロンプト(任意)。送信後は待たずに即返る
session_nameNoセッション名(省略で自動採番)
reasoning_effortNo指定不可(grok CLI の --effort は headless 専用で、対話 TUI では警告の上無視される。composer は effort 自体非対応)。指定すると起動前にエラーを返す

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
schemaYes
providerYes
session_idYes
event_cursorYes
wait_commandYes
submit_residueYes
managed_completionYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description fully carries the burden. It discloses non-blocking behavior, completion handling (exit codes, timeout), result collection via pty_read, and the error condition for reasoning_effort. This is highly transparent for a complex tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized for the tool's complexity. It is front-loaded with the main purpose and provides detailed guidance without unnecessary repetition. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and the presence of an output schema, the description covers all necessary aspects: launching, interaction, completion, parameter behavior, and error conditions. It assumes knowledge of related tools but is complete for the intended purpose.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description integrates parameters into the workflow but does not add significant new information beyond the schema descriptions. For example, reasoning_effort's error behavior is already detailed in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool launches a TUI for the Composer model. It specifies the verb '起動する' (launch) and the resource 'Composer モデル'. It distinguishes itself from sibling tools like pty_open and pty_list by being a higher-level agent launcher with a specific workflow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explains the workflow: launch, then use pty_send for turns, and handle completions via wait_command or aiterm-wait. It mentions that reasoning_effort is not supported and will error. However, it does not explicitly state when to use this tool versus alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/kitepon-rgb/aiterm-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server